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On the variance of intermittent demand estimates

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  • Syntetos, Aris A.
  • Boylan, John E.

Abstract

Intermittent demand occurs at random with many time periods showing no demand at all. Forecasting such demand patterns constitutes a challenging exercise because of the associated dual source of variation (demand intervals and demand sizes). Research in this area has developed rapidly in recent years with new results implemented into supply chain software solutions because of its practical implications. In an inventory context, both the accuracy of the forecasts and their variability (sampling error of the mean) have equal importance in terms of service level achievement and/or inventory cost minimisation. Although the former issue has been studied extensively (mainly building upon Croston's model, 1972) the latter has been largely ignored. The purpose of this paper is to analyse the most well-cited intermittent demand estimation procedures in terms of the variance of their estimates. Detailed derivations are offered along with a discussion of the underlying assumptions. As such, we hope that our contribution may constitute a point of reference for further analytical work in this area as well as facilitate a better understanding of issues related to modelling intermittent demands.

Suggested Citation

  • Syntetos, Aris A. & Boylan, John E., 2010. "On the variance of intermittent demand estimates," International Journal of Production Economics, Elsevier, vol. 128(2), pages 546-555, December.
    • Handle: RePEc:eee:proeco:v:128:y:2010:i:2:p:546-555
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    Cited by:

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    2. Mekhtiev, Mirza Arif, 2013. "Analytical evaluation of lead-time demand in polytree supply chains with uncertain demand, lead-time and inter-demand time," International Journal of Production Economics, Elsevier, vol. 145(1), pages 304-317.
    3. Lowas, Albert F. & Ciarallo, Frank W., 2016. "Reliability and operations: Keys to lumpy aircraft spare parts demands," Journal of Air Transport Management, Elsevier, vol. 50(C), pages 30-40.
    4. Zhu, Sha & Dekker, Rommert & van Jaarsveld, Willem & Renjie, Rex Wang & Koning, Alex J., 2017. "An improved method for forecasting spare parts demand using extreme value theory," European Journal of Operational Research, Elsevier, vol. 261(1), pages 169-181.
    5. Costantino, Francesco & Di Gravio, Giulio & Patriarca, Riccardo & Petrella, Lea, 2018. "Spare parts management for irregular demand items," Omega, Elsevier, vol. 81(C), pages 57-66.
    6. Zied Babai, Mohamed & Syntetos, Aris & Teunter, Ruud, 2014. "Intermittent demand forecasting: An empirical study on accuracy and the risk of obsolescence," International Journal of Production Economics, Elsevier, vol. 157(C), pages 212-219.

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